Themachine epsilon,, can be defined as the smallest floating point (with base 2) such that 1 + 1, that is, 1 +/2 == 1 in machine precision.(a) Write a function that returns this value by starting ateps = 1and iterativelydividing by 2 until the definition is satisfied.(b) Write a function that computesf(x) = log(1 + exp(x)) and then evaluate: f(0),f(80),f(80), andf(800).(c) How would you specify your function to avoid computations ifx0 (x 0 and|x|is large)? (Hint: .)(d) How would you implement your function to not overflow ifx0?(^1) 1e201, of course.(QR via Gram-Schmidt) IfA= [a 1 a 2 ap] is an-by-pmatrix withn pthen we can obtain a thin QR decomposition ofAusing Gram-Schmidt orthogonalization^2. To getQ, we start withu 1 =a 1 , then compute iteratively, fori= 2, ,p,ui=aii^1j=projuj(ai),and finally, withqi=ui/ui, setQ= [q 1 q 2 qp] as a column-normalized versionofU= [ui]i=1,...,p.(a) Show thatC =Q Ais upper triangular and thatC is the Cholesky factor of(b) Write a R function that computes theQorthogonal factor of a Vandermondematrix with base vector x and degree d without computing the Vandermondematrix explicitly, that is, as your function iterates to computeui, compute anduse the columns of the Vandermonde matrix on the fly.(c) It can be shown that with 1 = 1,i+1=u iui, and i=u iDiag(x)uiu iui, fori= 1,...,d+ 1,where Diag(x) is a diagonal matrix with diagonal entriesx, theU matrix canbe computed using the recurrence relation: u 1 = (^1) n,u 2 =x 11 n, and, for i= 2, ,d, (ui+1)j= (xji)(ui)j i+ i (ui 1 )j, forj= 1, ,n. Write a R function that, givenand, computesQ. (d) Now modify your function in (a) to also computeand. Quick check for the compact representation ofQ: show that 1 =x , 2 =n, and 3 = (n1)s^2 x, where xands^2 xare the sample mean and variance ofxrespectively.(Orthogonal staged regression) Suppose we observeyN(X,^2 In),Xan-by-pfull rank matrix, and wish to testH 0 :j=j+1==p= 0for some j 1. It can be shown that the ML estimator = (X X)^1 X y hasdistributionN(,^2 (X X)^1 ).(a) IfXhas thin QR decompositionX=QR, show thatH 0 is equivalent to testingj==p= 0 where=R, and so we can regressyonQinstead ofX, thatis, estimatefromyN(Q,^2 In).(^2) This is not recommended in practice because it is numerically unstable. Orthogonalizations are usually performed using Givens rotations or Householder reflections.(b) Show that the ML estimator for is = Q y and the components of areindependent.(c) UsingR, explain how you compute: (i) the ML estimateas a function of ,and (ii) the correlation matrix ofusing onlycrossprod,normalize.cols, andinv.upper.tri.(d) As a concrete example, let us use thecarsdataset^3 to fit a polynomial regressionof degreed= 3. Takedistto be the responseyandspeedto be the base vectorxto defineX=V(x,d), a Vandermonde design.i. ComputeQusing the routine from 3.b, obtain =Q yand compare it tothe estimate fromcoef(lm(dist ~ Q - 1)).ii. Computeaccording to (c) and compare it to the estimate fromcoef(lm(dist ~ vandermonde(speed, 3) - 1))iii. () Compare thep-values when testingj = 0 andj = 0 forj= 1,..., 4from the above regression fits. How would you explain the discrepancies basedon the results from (b) and (c)? What degree would you recommend for apolynomial regression ofdistonspeed?(e) () Suppose we use another routine to obtain a QR decomposition ofX. Underwhat conditions isRthe Cholesky factor ofX X? Write a function that returnsa QR decomposition ofXsatisfying these conditions.(^3) Load it in R withdata(cars).